This paper discusses new mobile usage scenarios for WLAN technologies and presents an architecture that is based on the notion of intermittent connectivity instead of seamless connectivity. The Drive-thru Internet approach is intended to support Internet applications of mobile users in environments where no permanent connectivity is available, a common case for nomadic users. We have chosen the extreme scenario of users in vehicles moving at high speed on the road and provide connectivity by means of WLAN access points. Our service architecture takes the transient character of local network access into account and provides for persistent transport connections and application layer mobility. From reviewing common Internet applications, we derive application-specific extensions to optimise various kinds of protocols and provide a concrete usage example. We also discuss the relation of Drive-thru Internet to technologies such as network layer mobility, authenticated network access, common WLAN hot spot setups, and WLAN roaming.

Summary. Most requests on the Web are made on behalf of human users, and like other human-computer interactions, the actions of the user can be characterized by identifiable regularities. Much of these patterns of activity, both within a user, and between users, can be identified and exploited by intelligent mechanisms for learning Web request patterns. Our focus is on Markov-based probabilistic techniques, both for their predictive power and their popularity in Web modeling and other domains. Although history-based mechanisms can provide strong performance in predicting future requests, performance can be improved by including predictions from additional sources. In this chapter we review the common approaches to learning and predicting Web request patterns. We provide a consistent description of various algorithms (often independently proposed), and compare performance of those techniques on the same data sets. We also discuss concerns for accurate and realistic evaluation of these techniques. 1

This paper presents the design and implementation of a Web prefetching module in Mozilla, an open-source and cross-platform browser. We have incorporated two kinds of predictors: a historybased predictor and a content-based predictor. These two predictors can analyze a user’s behavior and the contents of recent HTML pages to predict likely next links; thus, they provide personalized predictions which are then utilized to determine which resources should be prefetched. 1

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